Presented at the II International Summer School for Rare Disease and Orphan Drug Registries, September 15-19, 2014, Organized by the National Centre for Rare Diseases Istituto Superiore di Sanità (ISS), Rome, Italy. Note the extensive contribution by many consortium members and partners listed in the acknowledgements slide.

1. Melissa
Haendel
Sept 19th,
2014
THE APPLICATION OF
THE HUMAN
PHENOTYPE
ONTOLOGY
II International Summer School
RARE DISEASE AND ORPHAN
DRUG REGISTRIES

2. OUTLINE
 Why phenotyping is hard
 About Ontologies
 Diagnosing known diseases
 Getting the phenotype data
 How much phenotyping is enough?
 Model organism data for undiagnosed
diseases

3. PHENOTYPIC BEGINNINGS
http://rundangerously.blogspot.com/2010/06/inconvenient-summer-
head-cold.html http://www.vetnext.com/images http://commons.wikimedia.org/wiki/File:CleftLip1.png
Phenotyping SEEMS like a simple task, but there are shades of
grey and nuances that are difficult to convey.

4. http://anthro.palomar.edu/abnormal/abnormal_4.htm
http://www.pyroenergen.com/articles07/downs-syndrome.htm
http://www.theguardian.com/commentisfree/2009/oct/27/downs-syndrome-increase-terminations

5. THE CONSTELLATION OF PHENOTYPES SIGNIFIES THE DISEASE –
A ‘PROFILE’
http://www.learn.ppdictionary.com/prenatal_development_2.htm
http://www.pyroenergen.com/articles07/do
wns-syndrome.htm
http://www.theguardian.com/commentisfree/2009/oct/27/downs-syndrome-increase-terminations
http://anthro.palomar.edu/abnormal/abnormal_4.htm

6. CLINICAL PHENOTYPING
Often free text or checkboxes
Dysmorphic features
• df
• dysmorphic
• dysmorphic faces
• dysmorphic features
Congenital malformation/anomaly:
• congenital anomaly
• congenital malformation
• congenital anamoly
• congenital anomly
• congential anomaly
• congentital anomaly
• cong. m.
• cong. Mal
• cong. malfor
• congenital malform
• congenital m.
• multiple congenital anomalies
• multiple congenital abormalities
• multiple congenital abnormalities
Examples of lists:
* dd. cong. malfor. behav. pro.
* dd. mental retardation
* df< delayed puberty
* df&lt
* dd df mr
* mental retar.short stature

7. SEARCHING FOR PHENOTYPES USING
TEXT ALONE IS INSUFFICIENT
OMIM Query # Records
“large bone” 785
“enlarged bone” 156
“big bone” 16
“huge bones” 4
“massive bones” 28
“hyperplastic bones” 12
“hyperplastic bone” 40
“bone hyperplasia” 134
“increased bone growth” 612

8. TERMS SHOULD BE WELL DEFINED SO
THEY GET USED PROPERLY
We need to capture synonyms and use unique
labels

9. SO WHAT IS THE PROBLEM?
 Obviously similar phenotype descriptions mean the same
thing to you, but not to a computer:
 generalized amyotrophy
 generalized muscle, atrophy
 muscular atrophy, generalized
 Many publications have little information about the actual
phenotypic features seen in patients with particular mutations
 Databases cannot talk to one another about phenotypes

10. OUTLINE
 Why phenotyping is hard
 About Ontologies
 Diagnosing known diseases
 Getting the phenotype data
 How much phenotyping is enough?
 Model organism data for undiagnosed
diseases

11. ONTOLOGIES CAN HELP.
A controlled vocabulary of logically defined,
inter-related terms used to annotate data
 Use of common or logically related terms across
databases enables integration
 Relationships between terms allow annotations
to be grouped in scientifically meaningful ways
 Reasoning software enables computation of
inferred knowledge
 Some well known ontologies are SNOMED-CT,
Foundational Model of Anatomy, Gene Ontology,
Linnean Taxonomy of species

12. OTHER COMMON USES OF ONTOLOGIES

13. HUMAN PHENOTYPE ONTOLOGY
Used to annotate:
• Patients
• Disorders
• Genotypes
• Genes
• Sequence variants
Abnormality of
pancreatic islet
cells
Reduced pancreatic
beta cells
Abnormality of endocrine
pancreas physiology
Pancreatic islet
cell adenoma
Abnormality of exocrine
pancreas physiology
Pancreatic islet cell
adenoma
Insulinoma
Multiple pancreatic
beta-cell adenomas
Mappings to SNOMED-CT,
UMLS, MeSH, ICD, etc.
Köhler et al. The Human Phenotype Ontology project: linking molecular biology and
disease through phenotype data. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.

14. USING A CONTROLLED VOCABULARY TO LINK
PHENOTYPES TO DISEASES
Failure to
Thrive
Chromosome
21 Trisomy
Flat Head
Abnormal
Ears
Umbilical
Hernia
Broad Hands

15. SURVEY OF ANNOTATIONS IN DISEASE CORPUS
7000+ diseases OMIM +
Orphanet + Decipher
(ClinVar coming soon)
111,000+ annotations
Phenotype annotations are unevenly distributed across
different anatomical systems

16. HOW DOES HPO RELATE TO OTHER
CLINICAL VOCABULARIES?
Winnenburg and Bodenreider, ISMB PhenoDay, 2014

17. LOGICAL TERM DEFINITION
Definitions are of the following Genus-Differentia form:
X = a Y which has one or more differentiating characteristics.
where X is the is_a parent of Y.
Definition of a cylinder:
Surface formed by the set of lines
perpendicular to a plane, which pass
through a given circle in that plane.
is_a is_a
Definition: Blue cylinder = Cylinder that has color blue.
Definition: Red cylinder = Cylinder that has color red.

18. ABOUT REASONERS
A piece of software able to infer logical consequences
from a set of asserted facts or axioms.
They are used to check the logical consistency of the
ontologies and to extend the ontologies with "inferred"
facts or axioms
For example, a reasoner would infer:
Major premise: All mortals die.
Minor premise: Some men are mortals.
Conclusion: Some men die.

19. PHENOTYPES CAN BE CLASSIFIED IN
MULTIPLE WAYS
Abnormality
of the eye
Abnormal eye
morphology
Vitreous
hemorrhage
Abnormality of the
cardiovascular
system
Abnormal eye
physiology
Hemorrhage
of the eye
Internal
hemorrhage
Abnormality
of the globe
Abnormality of
blood circulation

20. PHENOTYPE MATCHING
Patient Phenotype
Resting tremors
REM disorder
Unstable posture
Myopia
Neuronal loss in
Substantia Nigra
Phenotype of known
variant
Resting tremors
REM disorder
Unstable posture
Nystagmus
Neuronal loss in
Substantia Niagra

21. abn. of
the eye
abn. of the
ocular region
abn. of the
eyelid abn. of globe
localization or size
abn. of the
palpebral fissures
hypertelorism
downward slanting
palpebral fissures
Noonan Syndrome
a)
Syndrome term
Query term
Overlap between
query and disease
abn. of
the eye
abn. of the
ocular region
abn. of the
eyelid abn. of globe
localization or size
abn. of the
palpebral fissures
downward slanting
palpebral fissures
Opitz Syndrome
b)
telecanthus
hypertelorism
c)
Noonan Syndrome
downward slanting
palpebral fissures
hypertelorism
3.78
3.05
Opitz Syndrome
hypertelorism
telecanthus
3.05
Query (Q)
hypertelorism
2.45
downward slanting
palpebral fissures
(IC of abn. of the eyelid)
sim(Q,Noonan) = 3.78 + 3.05
sim(Q,Opitz) =2.45 + 3.05
2
= 2.75
2
= 3.42

22. COMPARATIVE VISUALIZATONS

23. OUTLINE
 Why phenotyping is hard
 About Ontologies
 Diagnosing known diseases
 Getting the phenotype data
 How much phenotyping is enough?
 Model organism data for undiagnosed
diseases

24. THE YET-TO-BE DIAGNOSED PATIENT
 Known disorders not recognized during
prior evaluations?
 Atypical presentation of known
disorders?
 Combinations of several disorders?
 Novel, unreported disorder?

25. EXOME ANALYSIS
Remove off-target, common variants,
and variants not in known disease
causing genes
Recessive, de novo filters
http://compbio.charite.de/PhenIX/
Target panel of 2741 known
Mendelian disease genes
Compare
phenotype
profiles using
data from:
HGMD, Clinvar,
OMIM, Orphanet
Zemojtel et al. Sci Transl Med 3 September 2014:
Vol. 6, Issue 252, p.252ra123

26. PHENIX PERFORMANCE TESTING
Figure removed due to restrictions. Please see the paper:
http://stm.sciencemag.org/content/6/252/252ra123.full
Simulated datasets created by spiking DAG panel generated VCF file with the causative
mutation removed

27. CONTROL PATIENTS WITH KNOWN
MUTATIONS
Inheritance Gene Average
Rank
AD ACVR1, ATL1, BRCA1, BRCA2, CHD7 (4),
CLCN7, COL1A1, COL2A1, EXT1, FGFR2 (2),
FGFR3, GDF5, KCNQ1, MLH1 (2), MLL2/KMT2D,
MSH2, MSH6, MYBPC3, NF1 (6), P63, PTCH1,
PTH1R (2), PTPN11 (2), SCN1A, SOS1, TRPS1,
TSC1, WNT10A
1.7
AR ATM, ATP6V0A2, CLCN1 (2), LRP5, PYCR1,
SLC39A4
5
X EFNB1, MECP2 (2), DMD, PHF6 1.8

28. WORKFLOW FOR CLINICAL EXOME
Suspected genetic
disease
DRG sequencing
Deep phenotyping
ANALYSIS
Top ranked
candidates
Clinical rounds
Exclude candidate
gene
Sanger validation
Cosegregation
studies
Fails
Diagnosis
Passes
Inconsistent
Consistent
Reconsider short list
Choose best
candidate
Variant Analysis
Annotation sources:
HGMD
MAF (dbSNP, ESP)
ClinVar
Prediction criteria:
Predicted pathogenicity
Variant class
Location in DRG target region
Computational Phenotype Analysis
HPO
Annotation sources:
OMIM, Orphanet,
MGI...
Semantic similarity
Mode of inheritance
Ontology:
Prediction criteria: MP

29. PHENIX HELPED DIAGNOSE 11/40 PATIENTS
global developmental delay (HP:0001263)
delayed speech and language development (HP:0000750)
motor delay (HP:0001270)
proportionate short stature (HP:0003508)
microcephaly (HP:0000252)
feeding difficulties (HP:0011968)
congenital megaloureter (HP:0008676)
cone-shaped epiphysis of the phalanges of the hand (HP:0010230)
sacral dimple (HP:0000960)
hyperpigmentated/hypopigmentated macules (HP:0007441)
hypertelorism (HP:0000316)
abnormality of the midface (HP:0000309)
flat nose (HP:0000457)
thick lower lip vermilion (HP:0000179)
thick upper lip vermilion (HP:0000215)
full cheeks (HP:0000293)
short neck (HP:0000470)

30. University of Queensland, University of Sydney

31. SKELETOME PATIENT ARCHIVE
 Integration with the HPO, Orphanet, and Monarch Initiative
 Automated phenotyping from clinical summaries
 Collaborative diagnosis
University of Queensland, University of Sydney

32. THE FACES OF RARE DISEASES
 Screening and
diagnosis
 Treatment moni toring
 Surgical planning and
audi t
 Genotype-phenotype
correlation
 Cross-species
comparisons
 Face to text conversion
for text mining
mm
Non-invasive, non-irradiating deeply precise
3D facial analysis
University of Western Australia

33. OUTLINE
 Why phenotyping is hard
 About Ontologies
 Diagnosing known diseases
 Getting the phenotype data
 How much phenotyping is enough?
 Model organism data for undiagnosed
diseases

34. PHENOTIPS & PHENOMECENTRAL

35. USING ONTOLOGIES IN THE CLINIC
 Ontologies are large (HPO has > 10,000 terms) and
difficult to navigate
 Mapping data to an ontology post-visit is time
consuming and prone to error
 Best time to phenotype using ontologies is during the
patient visit
 Goals of PhenoTips
 Make deep phenotyping simple
 Make it “faster than paper”

36. PhenomeCentral is a Matchmaker
 Lets you know about other similar patients
 Lets you easily connect with other users
Each Patient Record can be:
 Public – Anyone can see the record
 Private – Only specified users/consortia can see
the record
 Matchable – The record cannot be seen, but can
be “discovered” by users who submit similar
patients

37. STEP 1: ADD PATIENT
 Can use the interface
built into
PhenomeCentral
 Can export data
directly from a local
PhenoTips instance
 Add a vcf file (or list
of genes)
 Set each record as
Private, Public or
Matchable

38. STEP 2: SEE PATIENTS SIMILAR TO YOURS

39. STEP 3: CONTACT THE SUBMITTER OF
THE OTHER DATASET

40. OUTLINE
 Why phenotyping is hard
 About Ontologies
 Diagnosing known diseases
 Getting the phenotype data
 How much phenotyping is enough?
 Model organism data for undiagnosed
diseases

41. HOW MUCH PHENOTYPING IS ENOUGH?
 How many annotations…?
 How many different categories?
 How many within each?

42. Not everything that counts can be counted and not everything that can be
counted counts -Albert Einstein

43. METHOD: DERIVE BY CATEGORY
REMOVAL
 Remove annotations that are
subclasses of a single high-level node
 Repeat for each 1° subclass

44. Example: Schwartz-jampel Syndrome, Type I
to test influence of a single
phenotypic category

45. Example: Schwartz-jampel Syndrome
derivations to test influence of
a single phenotypic category

46. Schwartz-jampel Syndrome derivations

47. SEMANTIC SIMILARITY ALGORITHMS ARE
ROBUST IN THE FACE OF MISSING
INFORMATION
Similarity of Derived Disease to Original Derived Disease Profile Rank
 (avg) 92% of derived diseases are most-similar
to original disease
 Severity of impact follows proportion of
phenotype

48. METHOD: DERIVE BY LIFTING
 Iteratively map each class to their direct
superclass(es)
 Keep only leaf nodes

49. SEMANTIC SIMILARITY ALGORITHMS ARE
SENSITIVE TO SPECIFICITY OF INFORMATION
Similarity of Derived Disease to Original Derived Disease Profile Rank
 Severity of impact increases with more-general
phenotypes

50. ANNOTATION SUFFICIENCY SCORE
http://monarchinitiative.org/page/services

51. OUTLINE
 Why phenotyping is hard
 About Ontologies
 Diagnosing known diseases
 Getting the phenotype data
 How much phenotyping is enough?
 Model organism data for undiagnosed
diseases

52. WHAT TO DO WHEN WE CAN’T DIAGNOSE
WITH A KNOWN DISEASE?

53. MODELS RECAPITULATE VARIOUS
PHENOTYPIC ASPECTS OF DISEASE
B6.Cg-Alms1foz/fox/J
increased weight,
adipose tissue volume,
glucose homeostasis altered
ALSM1(NM_015120.4)
[c.10775delC] + [-]
GENOTYPE
PHENOTYPE
obesity,
diabetes mellitus,
insulin resistance
increased food intake,
hyperglycemia,
insulin resistance
kcnj11c14/c14;
insrt143/+(AB)
?

54. HOW MUCH PHENOTYPE DATA?
 Human genes have poor phenotype coverage
GWAS
+
ClinVar
+
OMIM

55. HOW MUCH PHENOTYPE DATA?
 Human genes have poor phenotype coverage
What else can we leverage?
GWAS
+
ClinVar
+
OMIM

56. HOW MUCH PHENOTYPE DATA?
 Human genes have poor phenotype coverage
What else can we leverage? …animal models
Orthology via PANTHER v9

57. COMBINED, HUMAN AND MODEL PHENOTYPES
CAN BE LINKED TO >75% HUMAN GENES
Orthology via PANTHER v9

58. EACH MODEL CONTRIBUTES DIFFERENT
PHENOTYPES
Data from MGI, ZFIN, & HPO, reasoned over with cross-species phenotype
ontology
https://code.google.com/p/phenotype-ontologies/

59. PROBLEM: CLINICAL AND MODEL
PHENOTYPES ARE DESCRIBED DIFFERENTLY

60. SOLUTION: BRIDGING SEMANTICS
anatomical
structure
endoderm of
forgut
lung bud
respiration organ
lung
organ
foregut
is_a (SubClassOf)
part_of
develops_from
capable_of
alveolus
organ part
alveolus of lung
FMA:lung
MA:lung
endoderm
GO: respiratory
gaseous exchange
MA:lung
alveolus
FMA:
pulmonary
alveolus
is_a (taxon equivalent)
NCBITaxon: Mammalia
EHDAA:
lung bud
only_in_taxon
pulmonary
acinus
alveolar sac
lung primordium
swim bladder
respiratory
primordium
NCBITaxon:
Actinopterygii
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012). Uberon, an integrative
multi-species anatomy ontology. Genome Biology, 13(1), R5. doi:10.1186/gb-2012-13-1-r5

61. PHENOTYPE REPRESENTATION REQUIRES
MORE THAN “PHENOT YPE ONTOLOGIES
Disease Gene Ontology Chemical
glucose
metabolism
(GO:0006006)
Gene/protein
function data
glucose
(CHEBI:172
34)
Metabolomics,
toxicogenomics
data
type II
diabetes
mellitus
(DOID:9352)
Disease &
phenotype
data
pyruvate
(CHEBI:153
61)
Cell
pancreatic
beta cell
(CL:0000169)
transcriptomic
data

62. MODELS BASED ON PHENOTYPIC
SIMILARITY
Washington, Haendel, et al. (2009). Linking Human Diseases to Animal Models Using Ontology-Based Phenotype
Annotation. PLoS Biol, 7(11). doi:10.1371/journal.pbio.1000247

63. OWLSIM: PHENOTYPE SIMILARITY
ACROSS PATIENTS OR ORGANISMS
find
Resting tremors
REM disorder
Shuffling gait
Unstable
posture
Neuronal loss in
Substant ia Nigra
Const ipat ion
Hyposmia
sterotypic
behavior
abnormal
EEG
decreased
stride length
poor rotarod
performance
axon
degenerat ion
decreased gut
peristalsis
failure to find
food
abnormal
motor function
sleep
disturbance
abnormal
locomotion
abnormal
coordination
CNS neuron
degenerat ion
abnormal
digestive
physiology
abnormal
olfaction
https://code.google.com/p/owltools/wiki/OwlSim

64. MONARCH PHENOTYPE DATA
Species Data source Genes Genotypes Variants Phenotype
annotations
Also in the system: Rat; IMPC; GO annotations; Coriell cell lines; OMIA; MPD; Yeast; CTD; GWAS;
Panther, Homologene orthologs; BioGrid interactions; Drugbank; AutDB; Allen Brain …157 sources
to date
Coming soon: Animal QTLs for pig, cattle, chicken, sheep, trout, dog, horse
Diseases
mouse MGI 13,433 59,087 34,895 271,621
fish ZFIN 7,612 25,588 17,244 81,406
fly Flybase 27,951 91,096 108,348 267,900
worm Wormbase 23,379 15,796 10,944 543,874
human HPOA 112,602 7,401
human OMIM 2,970 4,437 3,651
human ClinVar 3,215 100,523 445,241 4,056
human KEGG 2,509 3,927 1,159
human ORPHANET 3,113 5,690 3,064
human CTD 7,414 23,320 4,912

65. EXOMISER
METHOD Cohort VCF
file
Homo rec
De novo
dom
Compound
het
X-linked
Exomiser
Filters:
Mendelian
Frequency
Candidates
HP
https://www.sanger.ac.uk/resources/databa
ses/exomiser/query/exomiser2

66. EXOMISER RESULTS ON NIH UNDIAGNOSED
DISEASE PROGRAM PATIENTS
9 previously diagnosed families
Identified causative variants with a
rank of at least 7/408 potential
variants
21 families without identified
disorders
We have now prioritized variants in
STIM1, ATP13A2, PANK2, and CSF1R
in 5 different families (2 STIM1
families)
Bone et al., submitted 

67. UDP_2731
Patient
phenotypes Sh3kbp1 tm1Ivdi -/ -
Gait apraxia
Spasticity
Thyroid
stimulating
hormone excess
Behavioural/
Psychiatric
Abnormality
hyperactivity
hyperactivity
increased
dopamine level
increased
exploration in new
environment
abnormal
locomotor
behavior
Abnormal
voluntary
movement
Abnormality of
the endocrine
system
Behavioral
abnormality

68. WALKING THE INTERACTOME
Microcephaly
Myoclonus
Myoclonus
YARS
Microcephaly
Akinesia
Choreoathetosis
Microcephaly
musculoskeletal
movement
phenotype
Involuntary
movements
IL41L IARS
IARS2 MARS
AARS
Abnormal
stereopsis
Visual impairment
abnormal visual
perception
Patient
phenotypes
Combined Oxidative
Phosphorylation
Deficiency 14
FARS2
WARS2
?
AIMP1
UDP_1166

69. FINDING COLLABORATORS FOR
FUNCTIONAL VALIDATION
Patient
Phenotype profile
Phenotyping
experts

70. PHENOVIZ: INTEGRATE ALL HUMAN,
MOUSE, AND FISH DATA TO UNDERSTAND
CNVS
Desktop application
for differential
diagnostics in CNVs
 Explain manifestations of CNV diseases based on genes
contained in CNV
E.g., Supravalcular aortic stenosis in Williams syndrome can
be explained by haploinsufficiency for elastin
 Double the number of explanations using model data
Doelken, Köhler, et al. (2013) Dis Model Mech 6:358-72

71. A LOOK AT THE HPO

72. WHO USES THE HPO?
 Bayés, Àlex, et al. Nature
neuroscience 2011
 Castellano, Sergi, et al. PNAS
2014
 Corpas, Manuel, et al. " Current
Protocols in Human Genetics
2012
 Sifrim, Alejandro, et al. Nature
methods 2013
 Lappalainen, Ilkka, et al.
Nucleic acids research 2013
 Firth, Helen V., and Caroline F.
Wright. Developmental
Medicine & Child Neurology
2011
 Many more…

73. ADVANTAGES OF HPO
 Widely used, flexible, freely available, and community
supported resource
 Prioritization of candidate variants through tools such as
PhenIX and Exomizer, and others
 Extensive links to model organism ontologies, allowing
selection of optimal models for wet-lab validation and
research, and collaborators
 Intuitive clinical interfaces built into tools such as
PhenoTips, Certagenia, and others
 Ability to easily share data with key international projects
(Decipher/DDD, RD-Connect, PhenomeCentral,
Matchmaker Exchange, etc.)

74. LIMITATIONS
 Quantitative vs. qualitative – Much of clinical data is
quantitative lab data with reference standards. It is possible
to convert based on ±3 SD, but no way to record the
reference measure/population yet.
 Temporal presentation – ontologies can support temporal
ordering, but data capture tools don’t yet capture this and
the comparison algorithms don’t yet take it into account
 Severity – semantic encoding is available, but simple in
comparison to phenotype-specific measures
 Emerging ontology – some areas have poor coverage, such
as nervous system, behavior, and imaging results. Need to
represent the assays in these contexts.

75. ACKNOWLEDGMENTS
NIH-UDP
William Bone
Murat Sincan
David Adams
Amanda Links
David Draper
Joie Davis
Neal Boerkoel
Cyndi Tif f t
Bill Gahl
OHSU
Nicole Vasilesky
Matt Brush
Bryan Laraway
Shahim Essaid
Lawrence Berkeley
Nicole Washington
Suzanna Lewis
Chris Mungall
UCSD
Amarnath Gupta
Jef f Grethe
Anita Bandrowski
Maryann Martone
U of Pitt
Chuck Boromeo
Jeremy Espino
Becky Boes
Harry Hochheiser
Sanger
Anika Oehl r ich
Jules Jacobson
Damian Smedley
Toronto
Mar ta Gi rdea
Sergiu Dumi t r iu
Heather Trang
Mike Brudno
JAX
Cynthia Smi th
Charité
Sebast ian Kohler
Sandra Doelken
Sebast ian Bauer
Peter Robinson
Funding:
NIH Office of Director: 1R24OD011883
NIH-UDP: HHSN268201300036C, HHSN268201400093P

76. WHERE TO GET HPO, AND HOW TO
REQUEST NEW CONTENT
We need you!
Browse in the following places:
http://www.human-phenotype-ontology.org/
http://purl.bioontology.org/ontology/HP
Get the file:
http://purl.obolibrary.org/obo/hp.owl
Request content:
https://sourceforge.net/p/obo/human-phenotype-requests/new/
More Documentation:
https://code.google.com/p/phenotype-ontologies/